https://github.com/praveena2j/awesome-Weakly-Supervised-Facial-Behavior-Analysis

A curated list of weakly supervised learning methods and datasets for facial behavior analysis.
https://github.com/praveena2j/awesome-Weakly-Supervised-Facial-Behavior-Analysis

List: awesome-Weakly-Supervised-Facial-Behavior-Analysis

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A curated list of weakly supervised learning methods and datasets for facial behavior analysis.

• Host: GitHub
• URL: https://github.com/praveena2j/awesome-Weakly-Supervised-Facial-Behavior-Analysis
• Owner: praveena2j
• Created: 2023-10-18T14:57:52.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-04T23:27:28.000Z (about 1 year ago)
• Last Synced: 2024-05-21T22:00:46.402Z (about 1 year ago)
• Topics: awesome, awesome-list
• Homepage:
• Size: 868 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• ultimate-awesome - awesome-Weakly-Supervised-Facial-Behavior-Analysis - A curated list of weakly supervised learning methods and datasets for facial behavior analysis. (Other Lists / Julia Lists)

README

        
# Awesome-Weakly Supervised Facial Behavior Analysis [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

A curated list of weakly supervised learning methods for facial behavior analysis in both classification and regression tasks based on our survey paper "Weakly Supervised Learning for Facial Behavior Analysis: A Review". In addition, this repository includes basic datasets widely used in the framework of weakly supervised learning for facial behavior analysis. 

[[arXiv]](https://arxiv.org/abs/2101.09858)

If you find this work useful in your research, please consider citing our work :pencil: and giving a star :star2: :

```bibtex

@article{granger2021weakly,

  title={Weakly supervised learning for facial behavior analysis: A review},

  author={Praveen, R. Gnana and Cardinal, Patrick and Granger, Eric},

  journal={arXiv preprint arXiv:2101.09858},

  year={2021}

}

```

👀 Update frequently!

## 📋 What's New

- [May. 2024] Create awesome-Weakly Supervised Facial Behavior Analysis repository.

- [Oct. 2024] Updated the list of papers.

## 👥 Contributing

Please feel free to refer this repository for your FER research/development and send me [pull requests](https://github.com/kdhht2334/awesome-SOTA-FER/pulls) or email [Praveen]([email protected]) to add links.

## Table of Contents

- [Classification](#seven-emotion) 

  - [Inexact Supervised Learning](#2024-c)

  - [Incomplete Supervised Learning](#2023-c)

  - [Inaccurate Supervised Learning](#2022-c)

- [Regression](#emotion-intensity) 

  - [Inexact Supervised Learning](#2025-b)

  - [Incomplete Supervised Learning](#2025-c)

## Classification 

### Inexact Supervised Learning 

| Paper | Venue | Year | Code | Task

| :---  | :---: | :---:  | :---:| :---:|

| [Weakly supervised pain localization using multiple instance learning](https://ieeexplore.ieee.org/document/6553762) | FG | 2013 | N/A | Pain |

| [Multi-instance Hidden Markov Model for facial expression recognition](https://ieeexplore.ieee.org/document/7163116) | FG | 2015 | N/A | Expression |

| [Learning pain from au combinations: A weakly supervised approach via multiple instance learning](https://ieeexplore.ieee.org/document/8887215) | IEEE TAFFC | 2022 | N/A | Pain |

| [Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization](https://www.upf.edu/documents/8407855/8409953/RMCMIL_BMVC2014.pdf/70cf3e1b-e407-31a7-434f-bbad149c9966) | BMVC | 2014 | N/A | Expression

| [Lomo: Latent ordinal model for facial analysis in videos](https://ieeexplore.ieee.org/document/7780971) | CVPR | 2016 | N/A | Expression

| [Identifying user-specific facial affects from spontaneous expressions with minimal annotation](https://ieeexplore.ieee.org/abstract/document/7308029) | IEEE TAFFC | 2016 | N/A | Expression

| [From Emotions to Action Units with Hidden and Semi-Hidden-Task Learning](https://ieeexplore.ieee.org/document/7410779) | ICCV | 2015 | N/A | AU

| [Exploring Domain Knowledge for Facial Expression-Assisted Action Unit Activation Recognition](https://ieeexplore.ieee.org/abstract/document/8329513) | IEEE TAFFC | 2020 | N/A | AU

| [Weakly supervised facial action unit recognition through adversarial training](https://ieeexplore.ieee.org/document/8578331) | CVPR | 2018 | N/A | AU

| [Weakly Supervised Facial Action Unit Recognition With Domain Knowledge](https://ieeexplore.ieee.org/abstract/document/8472814) | IEEE TCYB | 2018 | N/A | AU

| [Classifier Learning With Prior Probabilities for Facial Action Unit Recognition](https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Classifier_Learning_With_CVPR_2018_paper.html) | CVPR | 2018 | N/A | AU

### Incomplete Supervised Learning 

| Paper | Venue | Year | Code | Task

| :---  | :---: | :---:  | :---:| :---:|

| [A Weakly Supervised learning technique for classifying facial expressions](https://www.sciencedirect.com/science/article/abs/pii/S0167865518304161) | PRL | 2019 | N/A | Expressions

| [Towards Semi-Supervised Deep Facial Expression Recognition With an Adaptive Confidence Margin](https://ieeexplore.ieee.org/document/9880204) | CVPR | 2022 | [Github](https://github.com/hangyu94/Ada-CM) | Expressions

| [Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher](https://ieeexplore.ieee.org/abstract/document/9629313) | IEEE TAFFC | 2023 | N/A | Expressions

| [Margin-Mix: Semi–Supervised Learning for Face Expression Recognition](https://link.springer.com/chapter/10.1007/978-3-030-58592-1_1) | ECCV | 2020 | N/A | Expressions

| [Rethinking Pseudo-Labeling for Semi-Supervised Facial Expression Recognition With Contrastive Self-Supervised Learning](https://ieeexplore.ieee.org/abstract/document/10121455) | IEEE Access | 2023 | N/A | Expressions

| [Semi-supervised facial expression recognition using reduced spatial features and Deep Belief Networks](https://www.sciencedirect.com/science/article/abs/pii/S0925231219311579) | Neurocomputing | 2019 | N/A | Expressions

| [Exploiting Sparsity and Co-occurrence Structure for Action Unit Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163081) | FG | 2015 | N/A | AU

| [Multi-label learning with missing labels for image annotation and facial action unit recognition](https://www.sciencedirect.com/science/article/abs/pii/S0031320315000412) | PR | 2015 | N/A | AU

| [Facial action unit recognition under incomplete data based on multi-label learning with missing labels](https://www.sciencedirect.com/science/article/abs/pii/S0031320316301583) | PR | 2016 | N/A | AU

| [Handling missing labels and class imbalance challenges simultaneously for facial action unit recognition](https://link.springer.com/article/10.1007/s11042-018-6836-1) | MTA | 2019 | N/A | AU

| [Expression-assisted facial action unit recognition under incomplete AU annotation](https://www.sciencedirect.com/science/article/abs/pii/S0031320316301765) | PR | 2017 | N/A | AU

| [Dual Semi-Supervised Learning for Facial Action Unit Recognition](https://ojs.aaai.org/index.php/AAAI/article/view/4909) | AAAI | 2019 | N/A | AU

| [Deep Facial Action Unit Recognition From Partially Labeled Data](https://openaccess.thecvf.com/content_iccv_2017/html/Wu_Deep_Facial_Action_ICCV_2017_paper.html) | ICCV | 2017 | N/A | AU

| [Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition](https://proceedings.neurips.cc/paper_files/paper/2019/hash/cf67355a3333e6e143439161adc2d82e-Abstract.html) | NeurIPS | 2019 | [Github](https://github.com/nxsEdson/MLCR) | AU

### Inaccurate Supervised Learning 

| Paper | Venue | Year | Code | Task

| :---  | :---: | :---:  | :---:| :---:|

| [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Suppressing_Uncertainties_for_Large-Scale_Facial_Expression_Recognition_CVPR_2020_paper.html) | CVPR | 2020 | [Github](https://github.com/kaiwang960112/Self-Cure-Network) | Expressions

| [Relative Uncertainty Learning for Facial Expression Recognition](https://proceedings.neurips.cc/paper/2021/hash/9332c513ef44b682e9347822c2e457ac-Abstract.html) | NeurIPS | 2021 | [Github](https://github.com/zyh-uaiaaaa/Relative-Uncertainty-Learning) | Expressions

| [Dive Into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition](https://openaccess.thecvf.com/content/CVPR2021/html/She_Dive_Into_Ambiguity_Latent_Distribution_Mining_and_Pairwise_Uncertainty_Estimation_CVPR_2021_paper.html) | CVPR | 2021 | N/A | Expressions

| [Teaching with Soft Label Smoothing for Mitigating Noisy Labels in Facial Expressions](https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/2267_ECCV_2022_paper.php) | ECCV | 2022 | [Github](https://github.com/toharl/soft) | Expressions

| [Self-Paced Label Distribution Learning for In-The-Wild Facial Expression Recognition](https://dl.acm.org/doi/abs/10.1145/3503161.3547960) | ACM MM | 2022 | N/A | Expressions

| [Training deep networks for facial expression recognition with crowd-sourced label distribution](https://dl.acm.org/doi/abs/10.1145/2993148.2993165) | ACM ICMI | 2016 | N/A | Expressions

| [Facial Expression Recognition with Inconsistently Annotated Datasets](https://openaccess.thecvf.com/content_ECCV_2018/html/Jiabei_Zeng_Facial_Expression_Recognition_ECCV_2018_paper.html) | ECCV | 2018 | N/A | Expressions

| [MAN: Mining Ambiguity and Noise for Facial Expression Recognition in the Wild](https://www.sciencedirect.com/science/article/abs/pii/S0167865522003105) | PRL | 2022 | N/A | Expressions

| [Learn from All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition](https://link.springer.com/chapter/10.1007/978-3-031-19809-0_24) | ECCV | 2022 | [Github](https://github.com/zyh-uaiaaaa/Erasing-Attention-Consistency) | Expressions

| [Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild](https://openaccess.thecvf.com/content_cvpr_2017/html/Li_Reliable_Crowdsourcing_and_CVPR_2017_paper.html) | CVPR | 2017 | N/A | Expressions

| [Learning Facial Action Units From Web Images With Scalable Weakly Supervised Clustering](https://openaccess.thecvf.com/content_cvpr_2018/html/Zhao_Learning_Facial_Action_CVPR_2018_paper.html) | CVPR | 2018 | [Github](https://github.com/zkl20061823/WSC) | AU

| [Recognition of Action Units in the Wild with Deep Nets and a New Global-Local Loss](https://ieeexplore.ieee.org/document/8237690) | ICCV | 2017 | N/A | AU

## Regression 

### Inexact Supervised Learning 

| Paper | Venue | Year | Code | Task

| :---  | :---: | :---:  | :---:| :---:|

| [Multi-Instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation](https://link.springer.com/chapter/10.1007/978-3-319-54184-6_11) | ACCV | 2016 | N/A | Pain |

| [Deep Weakly Supervised Domain Adaptation for Pain Localization in Videos](https://ieeexplore.ieee.org/abstract/document/9320216) | FG | 2020 | N/A | Pain |

| [Deep domain adaptation with ordinal regression for pain assessment using weakly-labeled videos](https://www.sciencedirect.com/science/article/abs/pii/S026288562100072X) | IVC | 2021 | [Github](https://github.com/praveena2j/WSDAOR) | Pain |

| [Deep Recurrent Multi-instance Learning with Spatio-temporal Features for Engagement Intensity Prediction](https://dl.acm.org/doi/abs/10.1145/3242969.3264981) | ACM ICMI | 2018 | N/A | Expression |

| [Multi-Instance Dynamic Ordinal Random Fields for Weakly Supervised Facial Behavior Analysis](https://ieeexplore.ieee.org/abstract/document/8347018) | TIP | 2018 | N/A | AU |

| [Bilateral Ordinal Relevance Multi-Instance Regression for Facial Action Unit Intensity Estimation](https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Bilateral_Ordinal_Relevance_CVPR_2018_paper.html) | CVPR | 2018 | N/A | AU |

### Incomplete Supervised Learning 

| Paper | Venue | Year | Code | Task

| :---  | :---: | :---:  | :---:| :---:|

| [Facial Expression Intensity Estimation Using Ordinal Information](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Zhao_Facial_Expression_Intensity_CVPR_2016_paper.html) | CVPR | 2016 | N/A | Expressions

| [Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation](https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Weakly-Supervised_Deep_Convolutional_CVPR_2018_paper.html) | CVPR | 2018 | N/A | AU

| [Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation](https://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Joint_Representation_and_Estimator_Learning_for_Facial_Action_Unit_Intensity_CVPR_2019_paper.html) | CVPR | 2019 | N/A | AU

| [Context-Aware Feature and Label Fusion for Facial Action Unit Intensity Estimation With Partially Labeled Data](https://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Context-Aware_Feature_and_Label_Fusion_for_Facial_Action_Unit_Intensity_ICCV_2019_paper.html) | ICCV | 2019 | N/A | AU

| [Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units](https://ieeexplore.ieee.org/abstract/document/8867879) | IEEE Access | 2019 | N/A | AU

| [Deep Facial Action Unit Recognition and Intensity Estimation from Partially Labelled Data](https://ieeexplore.ieee.org/document/8705351) | IEEE TAFFC | 2021 | N/A | AU

| [Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning](https://openaccess.thecvf.com/content/ACCV2020/papers/Sanchez_Semi-supervised_Facial_Action_Unit_Intensity_Estimation_with_Contrastive_Learning_ACCV_2020_paper.pdf) | ACCV | 2020 | N/A | AU